Universität Stuttgart
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Item Open Access Towards improved targetless registration and deformation analysis of TLS point clouds using patch-based segmentation(2023) Yang, Yihui; Schwieger, Volker (Prof. Dr.-Ing. habil. Dr. h.c.)The geometric changes in the real world can be captured by measuring and comparing the 3D coordinates of object surfaces. Traditional point-wise measurements with low spatial resolution may fail to detect inhomogeneous, anisotropic and unexpected deformations, and thus cannot reveal complex deformation processes. 3D point clouds generated from laser scanning or photogrammetric techniques have opened up opportunities for an area-wise acquisition of spatial information. In particular, terrestrial laser scanning (TLS) exhibits rapid development and wide application in areal geodetic monitoring owing to the high resolution and high quality of acquired point cloud data. However, several issues in the process chain of TLS-based deformation monitoring are still not solved satisfactorily. This thesis mainly focuses on the targetless registration and deformation analysis of TLS point clouds, aiming to develop novel data-driven methods to tackle the current challenges. For most deformation processes of natural scenes, in some local areas no shape deformations occur (i.e., these areas are rigid), and even the deformation directions show a certain level of consistency when these areas are small enough. Further point cloud processing, like stability and deformation analyses, could benefit from the assumptions of local rigidity and consistency of deformed point clouds. In this thesis, thereby, three typical types of locally rigid patches - small planar patches, geometric primitives, and quasi-rigid areas - can be generated from 3D point clouds by specific segmentation techniques. These patches, on the one hand, can preserve the boundaries between rigid and non-rigid areas and thus enable spatial separation with respect to surface stability. On the other hand, local geometric information and empirical stochastic models could be readily determined by the points in each patch. Based on these segmented rigid patches, targetless registration and deformation analysis of deformed TLS point clouds can be improved regarding accuracy and spatial resolution. Specifically, small planar patches like supervoxels are utilized to distinguish the stable and unstable areas in an iterative registration process, thus ensuring only relatively stable points are involved in estimating transformation parameters. The experimental results show that the proposed targetless registration method has significantly improved the registration accuracy. These small planar patches are also exploited to develop a novel variant of the multiscale model-to-model cloud comparison (M3C2) algorithm, which constructs prisms extending from planar patches instead of the cylinders in standard M3C2. This new method separates actual surface variations and measurement uncertainties, thus yielding lower-uncertainty and higher-resolution deformations. A coarse-to-fine segmentation framework is used to extract multiple geometric primitives from point clouds, and rigorous parameter estimations are performed individually to derive high-precision parametric deformations. Besides, a generalized local registration-based pipeline is proposed to derive dense displacement vectors based on segmented quasi-rigid areas that are corresponded by areal geometric feature descriptors. All proposed methods are successfully verified and evaluated by simulated and/or real point cloud data. The choice of proposed deformation analysis methods for specific scenarios or applications is also provided in this thesis.Item Open Access Climate sensitivity of a large lake(2013) Eder, Maria Magdalena; Bárdossy, András (Prof. Dr. rer. nat. Dr.-Ing.)Lakes are complex ecosystems that are on the one hand more or less enclosed by defined borders, but are on the other hand connected to their environment, especially to their catchment and the atmosphere. This study is examinig the climate sensitivity of large lakes using Lake Constance as an example. The lake is situated in Central Europe at the northern edge of the Alps, at the boundary of Austria, Germany and Switzerland. The maximum depth is 235 m, the total surface area is 535 km³ and the total volume 48.45 km². The numerical simulations in this study have been performed with the lake model system ELCOM-CAEDYM. The model system was validated using three different data sets: Observations of a turbid underflow after a flood flow in the main tributary, a lake-wide field campaign of temperature and phytoplankton, and long term monitoring data of temperature and oxygen in the hypolimion. The model system proved to be able to reproduce the effects of a flood flow in the largest tributary,. A huge turbid underflow was observed flowing into the main basin after an intense rain event in the Alps in August 2005. A numerical experiment showed the influence of the earth’s rotation on the flow path of the riverine water within the lake. The model also reproduced the temperature evolution and distribution and to some extent the phytoplankton patchiness measured in spring 2007 during an intensive field campaign. The model reproduced the measured time series of temperature and oxygen in the deep hypolimnion measured in the years 1980-2000. This indicates, that the vertical mixing and the lake’s cycle of mixing and stratification was reproduced correctly. Based on the model set-up validated with long term monitoring data, climate scenario simulations were run. The main focus was on temperature and oxygen concentrations in the hypolimnion, the cycle of stratification and mixing, and the heat budget of the lake. The meteorological boundary conditions for the climate scenario simulations were generated using a weather generator instead of downscaling climate projections from Global Climate Models. This approach gives the possibility to change different characteristics of the climate independently. The resulting lake model simulations are ”what-if”-scenarios rather than predictions, helping to obtain a deeper understanding of the processes in the lake. The main results can be summarized as follows: An increase in air temperature leads to an increase in water temperature, especially in the upper layers. The deep water temperature increases as well, but not to the same extent as the temperature of the epilimnion. This results in an increased vertical temperature difference. Due to the non-linear shape of the temperature-density curve, the difference in density grows even stronger than the temperature difference. This results in enhanced stratification stability, and consequently in less mixing. Complete mixing of the lake becomes more seldom in a warmer climate, but even in the scenario simulations with air temperature increased by 5 °C, full circulation took place every 3-4 years. Less complete mixing events lead to less oxygen in the hypolimnion. Additionally, as many biogeochemical processes are temperature dependant, the oxygen consumption rate is larger in warmer water. In the context of this study, climate variability is defined as episodes with daily average air temperatures deviating from the long-term average for this day of year. The episodes can be described by their duration in days and their amplitude in °C. Changes in climate variability can have very different effects, depending on the average air and water temperatures. The effects are stronger in lakes with higher water temperatures: For the hypolimnetic conditions, the seasonality in warming is important: Increasing winter air temperatures have a much stronger effect on the water temperatures in the lake than increasing summer temperatures. The combined effects of a warmer climate and higher nutrient concentrations enhances oxygen depletion in the hypolimnion. Finally, it is discussed, to what extent the results of this study are transferrable to other lakes. The reactions of Lake Constance to climate change are determined by the physical, geographical and ecological characteristics of the lake. Hydrodynamic reactions are defined by the mixing type, water temperatures and the residence time of the water in the lake. Furthermore it is important that the lake is almost never completely ice-covered, and that there are only minor salinity differences. The reactions of the ecosystem are determined also by the oligotrophic state of the lake. Results of this study thus can be transferred to other deep, monomictic, oligotrophic fresh water lakes, as for example the other large perialpine lakes of glacial origin.Item Open Access Volcanic evolution of Southern Tenerife (Canary Islands) during the Pleistocene and Holocene(2009) Kröchert, Jörg; Buchner, Elmar (PD Dr.)The Canary Islands are a group of volcanic ocean islands in the Central Atlantic near the continental margin of northwest Africa. Tenerife, with a volcanic history of more than 12 Ma of subaerial eruptions, is the largest island of the Canaries and is situated in the centre of the Archipelago. The Quaternary Bandas del Sur Formation in the South of Tenerife comprises a complex sequence of pyroclastic rocks and lavas and is part of the southern rift zone. In contrast to the northwest and northeast rift zones on Tenerife, the southern rift zone comprises a number of characteristics with respect to the morphological features, eruption cyclicity, and the geochemistry of the volcanic deposits. Various flank eruptions of the Las Cañadas volcano associated with basaltic lavas and the formation of cinder cones within the Bandas del Sur are important volcanic units for understanding the explosive volcanic cycles during the Pleistocene on Tenerife. Paleomagnetic studies, geochemical analysis of major and trace elements, and two radioisotopic dating (K-Ar) have been carried out on prominent cinder cones, to determine their stratigraphic position. By combining the results with previous K-Ar data in the Literature, the cones and lavas can be subdivided into three stratigraphic units. Cinder cones that belong to the first unit show reverse magnetization and Y/Nb ratios between 0.37-0.41; cinder cones of the second unit show normal magnetization and Y/Nb ratios of <0.35. The third unit comprises cinder cones with normal magnetization and Y/Nb ratios of about 0.47. The first two units were constructed between ~0.948-0.779 Ma and 0.323-0.300 Ma. These units define volcanic cycles that culminated in violent Plinian eruptions. The third and youngest unit possibly marks the beginning of a further volcanic cycle that started ~0.095 Ma ago. In order to reconstruct the uplift history of Tenerife, numerous uplifted fossil beaches and tuff cones were investigated. In the North and Northeast of Tenerife, the positions of fossil beaches indicate stable conditions since 130 ka. The uplift rates in southern Tenerife (within the Bandas del Sur) amount to a minimum of 15 m since 778 ka at Montaña Pelada and to a maximum of up to 45 m since 10 ka in the area of El Médano, suggesting an asymmetrical uplift of the island complex. The uplift in the South could be caused by seismic activity or mass loss due to flank collapse events. However, uplift due to ascending magma is more plausible. The fossil beach deposits of the El Médano area exhibit tubular-shaped concretions and concretionary dykes. These sediment structures have been interpreted as the result of a) the interaction between hot ignimbrites that overflowed wet beaches, b) fast accumulation of beach sands on hot and degassing ignimbrites, c) paleoliquefaction caused by an earthquake (seismites). Based on the interpretation as seismites, an intense paleoearthquake was proposed to be responsible for the generation of the paleoliquefaction structures. However, the sedimentary structures in question show the general criteria diagnostic for rhizocretions and root tubules with respect to their orientation, size, branching system, and style of cementation. Faults of a well-defined strike direction that precisely coincides with the southern rift fault system occur in the El Médano site. This fault system was generated contemporaneously with a chain of cinder cones ~948 ka ago. Open fractures in ignimbrites (~668 ka) and the fossil beach deposits (~10 ka) of the El Médano area suggest that the rift-associated fault system has been seismically active in the aftermath and probably is still active. A further fault system striking perpendicular to the rift-associated faults probably originates from a Holocene paleoearthquake of moderate intensity. Earthquake-induced ground effects in the fossil beach deposits of the study area are consistent with seismically induced ground effects of several recent and well-documented earthquakes and gravitational sliding triggered by an intense earthquake in Nicoya/Costa Rica in 1990. Both, the rift-associated and the earthquake-induced fault system, initially produced open cracks in the fossil beach deposits that were occupied by plants and subsequently stabilized by cementation. These results accentuate that the densely settled southern part of Tenerife is latently endangered by volcanic and seismic activity, though, currently, there are no indications of increasing volcanic activity in this region. Uplift due to recent magma loading is not observable and the intensity of a paleoearthquake in the El Médano area was probably considerably lower than mentioned in the literature.Item Open Access Analyzing and characterizing spaceborne observation of water storage variation : past, present, future(2024) Saemian, Peyman; Sneeuw, Nico (Prof. Dr.-Ing.)Water storage is an indispensable constituent of the intricate water cycle, as it governs the availability and distribution of this precious resource. Any alteration in the water storage can trigger a cascade of consequences, affecting not only our agricultural practices but also the well-being of various ecosystems and the occurrence of natural hazards. Therefore, it is essential to monitor and manage the water storage levels prudently to ensure a sustainable future for our planet. Despite significant advancements in ground-based measurements and modeling techniques, accurately measuring water storage variation remained a major challenge for a long time. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On (GRACE-FO) satellites have revolutionized our understanding of the Earth's water cycle. By detecting variations in the Earth's gravity field caused by changes in water distribution, these satellites can precisely measure changes in total water storage (TWS) across the entire globe, providing a truly comprehensive view of the world's water resources. This information has proved invaluable for understanding how water resources are changing over time, and for developing strategies to manage these resources sustainably. However, GRACE and GRACE-FO are subject to various challenges that must be addressed in order to enhance the efficacy of our exploitation of GRACE observations for scientific and practical purposes. This thesis aims to address some of the challenges faced by GRACE and GRACE-FO. Since the inception of the GRACE mission, scholars have commonly extracted mass changes from observations by approximating the Earth's gravity field utilizing mathematical functions termed spherical harmonics. Various institutions have already processed GRACE(-FO) data, known as level-2 data in the GRACE community, considering the constraints, approaches, and models that have been utilized. However, this processed data necessitates post-processing to be used for several applications, such as hydrology and climate research. In this thesis, we evaluate various methods of processing GRACE(-FO) level-2 data and assess the spatio-temporal effect of the post-processing steps. Furthermore, we aim to compare the consistency between GRACE and its successor mission, GRACE-FO, in terms of data quality and measurement accuracy. By analyzing and comparing the data from these two missions, we can identify any potential discrepancies or differences and establish the level of confidence in the accuracy and reliability of the GRACE-FO measurements. Finally, we will compare the processed level-3 products with the level-3 products that are presently accessible online. The relatively short record of the GRACE measurements, compared to other satellite missions and observational records, can limit some studies that require long-term data. This short record makes it challenging to separate long-term signals from short-term variability and validate the data with ground-based measurements or other satellite missions. To address this limitation, this thesis expands the temporal coverage of GRACE(-FO) observations using global hydrological, atmospheric, and reanalysis models. First, we assess these models in estimating the TWS variation at a global scale. We compare the performance of various methods including data-driven and machine learning approaches in incorporating models and reconstruct GRACE TWS change. The results are also validated against Satellite Laser Ranging (SLR) observations over the pre-GRACE period. This thesis develops a hindcasted GRACE, which provides a better understanding of the changes in the Earth's water storage on a longer time scale. The GRACE satellite mission detects changes in the overall water storage in a specific region but cannot distinguish between the different compartments of TWS, such as surface water, groundwater, and soil moisture. Understanding these individual components is crucial for managing water resources and addressing the effects of droughts and floods. This study aims to integrate various data sources to improve our understanding of water storage variations at the continental to basin scale, including water fluxes, lake water level, and lake storage change data. Additionally, the study demonstrates the importance of combining GRACE(-FO) observations with other measurements, such as piezometric wells and rain-gauges, to understand the water scarcity predicament in Iran and other regions facing similar challenges. The GRACE satellite mission provides valuable insights into the Earth's system. However, the GRACE product has a level of uncertainty due to several error sources. While the mission has taken measures to minimize these uncertainties, researchers need to account for them when analyzing the data and communicate them when reporting findings. This thesis proposes a probabilistic approach to incorporate the Total Water Storage Anomaly (TWSA) data from GRACE(-FO). By accounting for the uncertainty in the TWSA data, this approach can provide a more comprehensive understanding of drought conditions, which is essential for decision makers managing water resources and responding to drought events.Item Open Access Rekonstruktion der Ablagerungsverhältnisse im nordalpinen Vorlandbecken Südwest-Deutschlands(2006) Maurer, Holger; Seyfried, Hartmut (Prof. Dr.)Während der Erdöl- und Ergasprospektion zu Beginn der 50er Jahre rückte das Nordalpine Vorlandbecken (Molassebecken) in den Mittelpunkt des Forschungsinteresses. In den kommenden Jahrzehnten wurden zahlreiche Tiefbohrungen im Molassebecken abgeteuft und somit die Grundlage für eine lithostratigraphische Gliederung der sedimentären Abfolge geschaffen. Bis zur heutigen Zeit gibt es nur wenige sedimentologische und sequenzstratigraphische Untersuchungen zum Ablagerungsmilieu der klastischen Sedimente im Nordalpinen Vorlandbecken Süddeutschlands. Das Ziel der vorliegenden kumulativen Arbeit war es, in verschiedenen Themenbereichen mit sedimentologischen, paläopedologischen, geophysikalischen und mathematischen Methoden die Ablagerungsverhältnisse im Molassebecken zu rekonstruieren. In fünf Veröffentlichungen werden verschiedene stratigraphische Altersabschnitte des Nordalpinen Vorlandbeckens in Südwestdeutschland behandelt. Der Schwerpunkt der Untersuchungen wurde dabei auf die ältesten und die jüngsten Sedimentabfolgen im Molassebecken gelegt, die gleichzeitig den Beginn und das Ende der sedimentären Vorlandbecken-Entwicklung repräsentieren.Item Open Access Forming a hybrid intelligence system by combining Active Learning and paid crowdsourcing for semantic 3D point cloud segmentation(2023) Kölle, Michael; Sörgel, Uwe (Prof. Dr.-Ing.)While in recent years tremendous advancements have been achieved in the development of supervised Machine Learning (ML) systems such as Convolutional Neural Networks (CNNs), still the most decisive factor for their performance is the quality of labeled training data from which the system is supposed to learn. This is why we advocate focusing more on methods to obtain such data, which we expect to be more sustainable than establishing ever new classifiers in the rapidly evolving ML field. In the geospatial domain, however, the generation process of training data for ML systems is still rather neglected in research, with typically experts ending up being occupied with such tedious labeling tasks. In our design of a system for the semantic interpretation of Airborne Laser Scanning (ALS) point clouds, we break with this convention and completely lift labeling obligations from experts. At the same time, human annotation is restricted to only those samples that actually justify manual inspection. This is accomplished by means of a hybrid intelligence system in which the machine, represented by an ML model, is actively and iteratively working together with the human component through Active Learning (AL), which acts as pointer to exactly such most decisive samples. Instead of having an expert label these samples, we propose to outsource this task to a large group of non-specialists, the crowd. But since it is rather unlikely that enough volunteers would participate in such crowdsourcing campaigns due to the tedious nature of labeling, we argue attracting workers by monetary incentives, i.e., we employ paid crowdsourcing. Relying on respective platforms, typically we have access to a vast pool of prospective workers, guaranteeing completion of jobs promptly. Thus, crowdworkers become human processing units that behave similarly to the electronic processing units of this hybrid intelligence system performing the tasks of the machine part. With respect to the latter, we do not only evaluate whether an AL-based pipeline works for the semantic segmentation of ALS point clouds, but also shed light on the question of why it works. As crucial components of our pipeline, we test and enhance different AL sampling strategies in conjunction with both a conventional feature-driven classifier as well as a data-driven CNN classification module. In this regard, we aim to select AL points in such a manner that samples are not only informative for the machine, but also feasible to be interpreted by non-experts. These theoretical formulations are verified by various experiments in which we replace the frequently assumed but highly unrealistic error-free oracle with simulated imperfect oracles we are always confronted with when working with humans. Furthermore, we find that the need for labeled data, which is already reduced through AL to a small fraction (typically ≪1 % of Passive Learning training points), can be even further minimized when we reuse information from a given source domain for the semantic enrichment of a specific target domain, i.e., we utilize AL as means for Domain Adaptation. As for the human component of our hybrid intelligence system, the special challenge we face is monetarily motivated workers with a wide variety of educational and cultural backgrounds as well as most different mindsets regarding the quality they are willing to deliver. Consequently, we are confronted with a great quality inhomogeneity in results received. Thus, when designing respective campaigns, special attention to quality control is required to be able to automatically reject submissions of low quality and to refine accepted contributions in the sense of the Wisdom of the Crowds principle. We further explore ways to support the crowd in labeling by experimenting with different data modalities (discretized point cloud vs. continuous textured 3D mesh surface), and also aim to shift the motivation from a purely extrinsic nature (i.e., payment) to a more intrinsic one, which we intend to trigger through gamification. Eventually, by casting these different concepts into the so-called CATEGORISE framework, we constitute the aspired hybrid intelligence system and employ it for the semantic enrichment of ALS point clouds of different characteristics, enabled through learning from the (paid) crowd.Item Open Access Dynamic water masks from optical satellite imagery(München : Verlag der Bayerischen Akademie der Wissenschaften, 2019) Elmi, Omid; Sneeuw, Nico (Prof. Dr.-Ing.)Investigation of the global freshwater system has a vital role in critical issues e.g. sustainable development of water resources, acceleration of the hydrological cycle, variability of global sea level. Measurement of river streamflow is vital for such investigations as it gives a reliable estimate of freshwater fluxes over the continents. Despite such importance, the number of river discharge gauging station has been decreasing. At the same time, information on the global freshwater system has been increasing because of various types of ground observations, water-use information and spaceborne geodetic observations. Nevertheless, we cannot answer properly crucial questions about the amount of freshwater available on a certain river basin, or the spatial and temporal dynamics of freshwater variations and discharge, or the distribution of world’s freshwater resources in the future. The lack of comprehensive measurements of surface water storage and river discharge is a major impediment for a realistic understanding of the hydrological water cycle, which is a must for answering the aforementioned questions. This thesis aims to improve the methods for monitoring the surface extent of inland water bodies using satellite images. Satellite imaging systems capture the Earth surface in a wide variety of spectral and spatial resolution repeatedly. Therefore satellite imagery provides the opportunity to monitor the spatial change in shorelines, which can serve as a way to determine the water extent. Each band of a multispectral image reveals a unique characteristic of the Earth surface features like surface water extent. However selecting the spectral bands which provide the relevant information is a challenging task. In this thesis, we analyse the potential of multispectral transformations like Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) to tackle this issue by condensing the information available in all spectral bands in just a few uncorrelated variables. Moreover, we investigate how the change between multispectral images at different epochs can be highlighted by using the transformations. This study proposes an automatic algorithm for extracting the lake water extent from MODIS images and generating dynamics lake masks. For improving the accuracy of the lake masks and computational efficiency of the algorithm, two masks are defined for limiting the search area. The restricting masks are developed according to DEM of the surrounding area together with a map of the long-term variation of pixel values. Subsequently, an unsupervised pixel-based classification algorithm is applied for defining the lake coastline. The algorithm particularly deals with the challenges of generating long time series of lake masks. We apply the algorithm on five lakes in Africa and Asia, each of which demonstrates a challenge for lake area monitoring. However in the validation section, we demonstrate that the algorithm can generate accurate dynamic lake masks. Rivers show diverse behaviour along their path due to the contribution of different parameters like gradient of the elevation, river slope, tributaries and river bed morphology. Therefore for generating accurate river reach mask, we need to consider additional sources of information apart from pixel intensity. The region-based classification algorithm that we propose in this study takes advantages of all types of available information including pixel intensity and spatial and temporal interactions. Markov Random Fields provide a flexible frame for interaction between different sources of data and constraint. To find the most probable configuration of the field, the Maximum A Posteriori solution for the MRF must be found. To this end, the problem is reshaped as an energy minimization. The energy function is minimized applying graph cuts as a powerful optimization technique. The uncertainty in the graph cuts solution is also measured by calculating the minimum marginal energies. The proposed method is applied to four rivers reaches with different hydrological characteristics. We validate the obtained river area time series by comparing with in situ river discharge and satellite altimetric water level time series. Moreover, in this study, we present river discharge estimation models using the generated river reach masks. Our aim is to find an empirical relationship between the average river reach width and river discharge. The statistics in the validation periods support the idea of using river width-discharge prediction models as a complementary technique to the other spaceborne geodetic river discharge prediction approaches.Item Open Access Statistical downscaling of extremes of precipitation in mesoscale catchments from different RCMs and their effects on local hydrology(2011) Alam, Muhammad Mahboob; Bardossy, Andras (Prof. Dr. rer. nat. Dr. -Ing.)Global climate models are the only available comprehensive tools for studying the affects of climate change on our earth in terms of changes in different meteorological and hydrological variables in future. Precipitation and temperature are two of the most important meteorological variables with regards to their affects on other meteorological (e.g. humidity, evaporation etc.) and hydrological (e.g. river runoff) variables and on human life (e.g. food fibre production, economy etc.). Among other important local and large scale phenomenon that affects the occurrence and amount of precipitation (and severity of temperature), geographical and topographical conditions perhaps play most important role in the behaviour of these variables in certain area. This makes the two variables more or less local phenomenons that need to be specifically studied for each area of interest individually. Unfortunately the scale at which global climate models (GCMs) operate is too large for any meaningful study to be performed related to future patterns of these two variables on local scale. Different methodologies have thus been developed to downscale (i.e. to increase the resolution of) GCM data to the local scale. The two broad categories of downscaling methodologies are statistical and dynamical downscaling. In statistical downscaling methodology, an attempt is made to develop a relationship between large scale GCM modelled variable (called predictor) and local scale observed/measured variable (called predictant). Assuming that in future this relationship will hold, the relationship is used to predict local scale predictand for future simulated scenarios of predictor. In dynamical downscaling (the so called regional climate models (RCMs)) on the other hand, an attempt is made to embed a complete physical model of more or less the same complexity as GCM, in a GCM and upon receiving values from GCM at its boundaries, recalculate all possible physical formulations at a much finer scale. The local conditions are thus taken in to account and the results are believed to be more suitable for local scale studies. Both downscaling methodologies have been extensively applied in climate change and impact studies around the world with varying degree of success and new techniques are consistently being developed to improve upon them. Both methodologies have associated advantages and disadvantages. While statistical downscaling is computationally much cheaper than RCMs, statistical downscaling is based on basic assumption of stationarity which is sometimes hard to justify. RCMs on the other hand although attempt to solve physical equations at local scale, does also inherit bias from the parent GCM. This thesis presents statistical downscaling methodology which attempts to correct for the biases that are inherited by different RCMs. Three different RCMs are considered for German part of Rhine basin and using bias correction methodology based on correction of quantiles of precipitation (and temperature for some studies), new scenarios of precipitation are developed. Further, a distributed version of conceptual hydrological model HBV is calibrated and validated for German part of Rhine basin and raw and downscaled RCM scenarios of precipitation are fed into the model to ascertain the future hydrological regime in face of climate change for this important river. The downscaling procedure briefly discussed above was applied in two ways. In the first case the statistical downscaling methodology was performed on RCM data without considering any constraint during quantile-quantile exchange between RCM control and scenario runs. In the second case, the quantile-quantile exchange was conditioned on occurrence of certain circulation pattern. It was briefly discussed above how precipitation (occurrence and amount) is conditioned by certain phenomenon. In addition to geographical and topographical location, precipitation also depends upon large scale circulation patterns. Thus it was assumed that conditioning the downscaling methodology also on circulation patterns would bring about better results. To realize above concept, classification of circulation patterns is performed. Fuzzy rule based classification methodology is used to classify circulation patterns. Two new methodologies of classification of circulation patterns are presented in this thesis. One is based on low flow conditions in rivers in the study area and the other is based on clustering of precipitation stations. The new classification methodology is believed to provide better classification of circulation patterns in that the difference between the individual classes is enhanced and similarity among the same class intensified. A classification analysis measure called wetness index was developed and used to identify critical circulation patterns among the classified circulation patterns. Critical circulation patterns were identified for extreme wet and dry conditions and it was shown that all extreme cases of floods and droughts are caused by identified critical CPs. This thesis also presents and applies another statistical downscaling methodology based on multivariate autoregressive model of order 1 (one). The methodology makes use of the classification of circulation patterns described above. The parameters of the autoregressive model depend upon the circulation patterns. The methodology is used for number of head catchments in southern and eastern Germany. Head catchments by definition have very quick response time to any significant precipitation event. They contribute quickly to the surface runoff and if they are head catchments of larger rivers, may also result in bigger flood events. Downscaling of precipitation was performed for these catchments by using mean sea level pressure (MSLP) as predictor and local station precipitation as predictant. The model was developed such that ensemble of daily precipitation could be produced. Thereby enabling one to estimate associated uncertainty. Finally drought analysis are performed for German part of Rhine basin using Palmer drought severity index. A FORTRAN routine is developed which can calculate different kind of drought indices such as Palmer drought severity index, Palmer hydrological drought index, and monthly moisture anomaly index for certain catchment. The program developed is also capable of simultaneously mapping the results. The mapping of results makes it possible to ascertain the severity of drought over the larger area. The analysis of drought is performed for observational gridded data set and for control and A1B scenarios of three different RCMs.Item Open Access Large-scale high head pico hydropower potential assessment(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2018) Schröder, Hans Christoph; Wieprecht, Silke (Prof. Dr.-Ing.)Due to a lack of site-related information, Pico hydropower (PHP) has hardly been a projectable resource so far. This is particularly true for large area PHP potential information that could open a perspective to increase the size of development projects by aggregating individual PHP installations. The present work is extending the capabilities of GIS based hydropower potential assessment into the PHP domain through a GIS based PHP potential assessment procedure that facilitates the discrimination of areas without high head PHP potential against areas with PHP potential and against areas with so called “favorable PHP potential”. The basic unit of the spatial output is determined by the underlying PHP potential definition of this work: a standardized PHP installation and the required hydraulic source, together called standard unit, are located on an area of one square kilometer. The gradation of the output is a consequence of the verification techniques. Several large area PHP potential field assessment methods, based on contemplative analysis techniques, are developed in this work. Field assessments were conducted in Yunnan Province/China, Costa Rica, Ecuador and Sri Lanka. The aim for all field assessments is to get a comprehensive view on the PHP potential distribution of the entire country/province. Application of the GIS based PHP potential assessment procedure is aimed at the global tropical and subtropical regions.Item Open Access Porosity and permeability alterations in processes of biomineralization in porous media - microfluidic investigations and their interpretation(Stuttgart : Eigenverlag des Instituts für Wasser- und Umweltsystemmodellierung der Universität Stuttgart, 2022) Weinhardt, Felix; Class, Holger (apl. Prof. Dr.-Ing)Motivation: Biomineralization refers to microbially induced processes resulting in mineral formations. In addition to complex biomineral structures frequently formed by marine organisms, like corals or mussels, microbial activities may also indirectly induce mineralization. A famous example is the formation of stromatolites, which result from biofilm activities that locally alter the chemical and physical properties of the environment in favor of carbonate precipitation. Recently, biomineralization gained attention as an engineering application. Especially with the background of global warming and the objective to reduce CO2 emissions, biomineralization offers an innovative and sustainable alternative to the usage of conventional Portland cement, whose production currently contributes significantly to global CO2 emissions. The most widely used method of biomineralization in engineering applications, is ureolytic calcium carbonate precipitation, which relies on the hydrolysis of urea and the subsequent precipitation of calcium carbonate. The hydrolysis of urea at moderate temperatures is relatively slow and therefore needs to be catalyzed by the enzyme urease to be practical for applications. Urease can be extracted from plants, for example from ground jack beans, and the process is consequently referred to as enzyme-induced calcium carbonate precipitation (ECIP). Another method is microbially induced calcium carbonate precipitation (MICP), which uses ureolytic bacteria that produce the enzyme in situ. EICP and MICP applications allow for producing various construction materials, stabilizing soils, or creating hydraulic barriers in the subsurface. The latter can be used, for example, to remediate leakages at the top layer of gas storage reservoirs, or to contain contaminant plumes in aquifers. Especially when remediating leakages in the subsurface, the most crucial parameter to be controlled is its intrinsic permeability. A valuable tool for predicting and planning field applications is the use of numerical simulation at the scale of representative elementary volumes (REV). For that, the considered domain is subdivided into several REV’s, which do not resolve the pore space in detail, but represent it by averaged parameters, such as the porosity and permeability. The porosity describes the ratio of the pore space to the considered bulk volume, and the permeability quantifies the ease of fluid flow through a porous medium. A change in porosity generally also affects permeability. Therefore, for REV-scale simulations, constitutive relationships are utilized to describe permeability as a function of porosity. There are several porosity-permeability relationships in the literature, such as the Kozeny-Carman relationship, Verma-Pruess, or simple power-law relationships. These constitutive relationships can describe individual states but usually do not include the underlying processes. Different boundary conditions during biomineralization may influence the course of porosity-permeability relationships. However, these relationships have not yet been adequately addressed. Pore-scale simulations are, in principle, very well suited to investigate pore space changes and their effects on permeability systematically. However, these simulations also rely on simplifications and assumptions. Therefore, it is essential to conduct experimental studies to investigate the complex processes during calcium carbonate precipitation in detail at the pore scale. Recent studies have shown that microfluidic methods are particularly suitable for this purpose. However, previous microfluidic studies have not explicitly addressed the impact of biomineralization on hydraulic effects. Therefore, this work aims to identify relevant phenomena at the pore scale to conclude on the REV-scale parameters, porosity and permeability, and their relationship. Contributions: This work comprises three publications. First, a suitable microfluidic setup and workflow were developed in Weinhardt et al. [2021a] to study pore space changes and the associated hydraulic effects reliably. This paper illustrated the benefits and insights of combining optical microscopy and micro X-ray computed tomography (micro XRCT) with hydraulic measurements in microfluidic chips. The elaborated workflow allowed for quantitative analysis of the evolution of calcium carbonate precipitates in terms of their size, shape, and spatial distribution. At the same time, their influence on differential pressure could be observed as a measure of flow resistance. Consequently, porosity and permeability changes could be determined. Along with this paper, we published two data sets [Weinhardt et al., 2021b, Vahid Dastjerdi et al., 2021] and set the basis for two other publications. In the second publication [von Wolff et al., 2021], the simulation results of a pore-scale numerical model, developed by Lars von Wolff, were compared to the experimental data of the first paper [Weinhardt et al., 2021b]. We observed a good agreement between the experimental data and the model results. The numerical studies complemented the experimental observations in allowing for accurate analysis of crystal growth as a function of local velocity profiles. In particular, we observed that crystal aggregates tend to grow toward the upstream side, where the supply of reaction products is higher than on the downstream side. Crystal growth during biomineralization under continuous inflow is thus strongly dependent on the locally varying velocities in a porous medium. In the third publication [Weinhardt et al., 2022a], we conducted further microfluidic experiments based on the experimental setup and workflow of the first contribution and published another data set [Weinhardt et al., 2022b]. We used microfluidic cells with a different, more realistic pore structure and investigated the influence of different injection strategies. We found that the development of preferential flow paths during EICP application may depend on the given boundary conditions. Constant inflow rates can lead to the development of preferential flow paths and keep them open. Gradually reduced inflow rates can mitigate this effect. In addition, we concluded that the coexistence of multiple calcium carbonate polymorphs and their transformations could influence the temporal evolution of porosity-permeability relationships.